Preoperative discrimination of absence or presence of myometrial invasion in endometrial cancer with an MRI-based multimodal deep learning radiomics model.
Objective: Accurate preoperative evaluation of myometrial invasion (MI) is essential for treatment decisions in endometrial cancer (EC). However, the diagnostic accuracy of commonly utilized magnetic resonance imaging (MRI) techniques for this assessment exhibits considerable variability. This study...
| Publicado en: | Abdominal Radiology Vol. 50; no. 7; pp. 3335 - 3347 |
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| Autores principales: | , , , , , , , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Jul2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=186105115&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 186105115 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 2366004X JT14 jtl: Abdominal Radiology issn: 2366004X maglogo: N pubinfo: dt: Jul2025 vid: 50 iid: 7 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 186105115 182036110 10.1007/s00261-024-04766-y 186105115 ppf: 3335 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Preoperative discrimination of absence or presence of myometrial invasion in endometrial cancer with an MRI-based multimodal deep learning radiomics model. aug: au: Chen, Yuan Ruan, Xiaohong Wang, Ximiao Li, Peijun Chen, Yehang Feng, Bao Wen, Xianyan Sun, Junqi Zheng, Changye Zou, Yujian Liang, Bo Li, Mingwei Long, Wansheng Shen, Yuan affil: https://ror.org/05d5vvz89 The First Affiliated Hospital of Jinan University, Guangzhou, China sug: ab: Objective: Accurate preoperative evaluation of myometrial invasion (MI) is essential for treatment decisions in endometrial cancer (EC). However, the diagnostic accuracy of commonly utilized magnetic resonance imaging (MRI) techniques for this assessment exhibits considerable variability. This study aims to enhance preoperative discrimination of absence or presence of MI by developing and validating a multimodal deep learning radiomics (MDLR) model based on MRI. Methods: During March 2010 and February 2023, 1139 EC patients (age 54.771 ± 8.465 years; range 24–89 years) from five independent centers were enrolled retrospectively. We utilized ResNet18 to extract multi-scale deep learning features from T2-weighted imaging followed by feature selection via Mann–Whitney U test. Subsequently, a Deep Learning Signature (DLS) was formulated using Integrated Sparse Bayesian Extreme Learning Machine. Furthermore, we developed Clinical Model (CM) based on clinical characteristics and MDLR model by integrating clinical characteristics with DLS. The area under the curve (AUC) was used for evaluating diagnostic performance of the models. Decision curve analysis (DCA) and integrated discrimination index (IDI) were used to assess the clinical benefit and compare the predictive performance of models. Results: The MDLR model comprised of age, histopathologic grade, subjective MR findings (TMD and Reading for MI status) and DLS demonstrated the best predictive performance. The AUC values for MDLR in training set, internal validation set, external validation set 1, and external validation set 2 were 0.899 (95% CI, 0.866–0.926), 0.874 (95% CI, 0.829–0.912), 0.862 (95% CI, 0.817–0.899) and 0.867 (95% CI, 0.806–0.914) respectively. The IDI and DCA showed higher diagnostic performance and clinical net benefits for the MDLR than for CM or DLS, which revealed MDLR may enhance decision-making support. Conclusions: The MDLR which incorporated clinical characteristics and DLS could improve preoperative accuracy in discriminating absence or presence of MI. This improvement may facilitate individualized treatment decision-making for EC. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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